The Power of Believing: Salient Belief Predictors of Exercise Behavior in Normal Weight, Overweight, and Obese Pregnant Women
Bibliographic record
Abstract
BACKGROUND: Nearly 50% of U.S. women enter pregnancy as overweight or obese (OW/OB). There is a critical need to understand how to motivate OW/OB pregnant women for exercise behavior to improve their health and reduce adverse pregnancy outcomes. PURPOSE: To examine salient Theory of Planned Behavior belief predictors of normal weight (NW) and OW/OB pregnant women's exercise behavior (EXB) across pregnancy. METHODS: Pregnant women (N = 357) self-reported their exercise beliefs and behavior during each pregnancy trimester. Pearson correlations were used to examine exercise beliefs-behavior associations. Stepwise regressions were used to identify trimester (TRI) 1 and TRI 2 belief predictors of TRI 2 and TRI 3 EXB, respectively, for each weight status group. Belief endorsement was examined to identify critical beliefs. RESULTS: TRI 1 EXB beliefs explained 58% of the total variance (22% NW, 36% OW/OB) in TRI 2 EXB. TRI 2 EXB beliefs explained 32% of the total variance (17% NW, 15% OW/OB) in TRI 3 EXB. Individual beliefs varied by weight status and trimester. Control beliefs emerged with the lowest endorsement; making them most critical to target for exercise interventions. CONCLUSION: Prenatal exercise interventions should be weight status specific and target salient beliefs/barriers unique to the pregnancy trimesters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".